Abstract: A method (100) for creating a model for positioning, the method (100) comprising: receiving (S150) a training dataset (1) comprising samples (10) of an estimation labeled type, each sample (10) of the estimation labeled type being associated with a measurement position, each sample (10) of the estimation labeled type comprising sensor data (12), measured at the measurement position associated with the sample (10), the sensor data (12) being characteristic of the measurement position associated with the sample (10); an estimated measurement position (14), being an estimate of the measurement position associated with the sample (10); an estimated accuracy (16), being an estimate of an accuracy of the estimated measurement position (14); training (S160) a machine learning model (20) to convert sensor data (12) to a position, wherein the training is based on the samples (10) of the estimation labeled type in the training dataset (1), whereby the model for positioning is created.

Abstract: A method for generating an indoor environment model of a building. The method comprises forming a transmitter location model for defining positions of transmitters in said building using gathered information for establishing transmitter locations. The method further comprises receiving signal strength indicative measurements determined for a number of transmitters using at least one electronic communications device wherein the signal strength indicative measurements are based on a signal which has varying signal propagation characteristics in the indoor environment The signal strength indicative measurements are acquired from a number of known locations in the building, identifying discrepancies of signal transmittance in said indoor environment based on said signal strength indicative measurements in relation to said transmitter location model.

Abstract: A method for generating a model of an environment enabling positioning within the environment is disclosed.